Deciphering the language of fungal pathogen recognition receptors

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Submitted on 6 Jul 2015

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Deciphering the language of fungal pathogen recognition receptors

Witold Dyrka, Pascal Durrens, Sven J Saupe, Mathieu Paoletti, David James Sherman

To cite this version:

Witold Dyrka, Pascal Durrens, Sven J Saupe, Mathieu Paoletti, David James Sherman. Deciphering the language of fungal pathogen recognition receptors. EMBO Young Scientists Forum 2015, Jul 2015, Warsaw, Poland. 2015. �hal-01171745�

Deciphering the language of fungal

pathogen recognition receptors

Witold Dyrka

_{, Pascal Durrens}

_{, Sven J. Saupe}

_{, Mathieu Paoletti}

_{and David J. Sherman}

1

_{INRIA-Université Bordeaux-CNRS, Team MAGNOME, Talence, France,}

2

_{Institut de Biochimie et de Génétique Cellulaire, CNRS-Université de Bordeaux, France,}

_{Department of Biomedical Engineering, Wroclaw University of Technology, Poland}

*

witold.dyrka@pwr.edu.pl

_{mathieu.paoletti@ibgc.cnrs.fr}

_{david.sherman@inria.fr}

Computational model of repeats rearrangement

Stochastic string rewriting system with constraints  , R, P, Q   – alphabet of 20 amino acid types

R – set of rewriting rules u → v  * P – set of rule probabilities

Q – set of constraints, e.g.

allowed positions and lengths of crossing-overs

external constraints acting on repeats („selective pressure”)

This research was partially funded by ANR-11-BSV3-0019

Key properties of the model

It is easy to show that

for realistic parametrization of the crossing-over and mutation and simple constraints

the system generates a single stationary distribution of: amino-acid composition,

repeat number, repeat sequence.

Therefore, differences between distributions generated by the model

and observed in the reality

can be interpreted as an effect of external pressures. Fungi are genuine interactors which have

to deal constantly with multiple hostile non-self. It has been proposed that their ultimate line

of defense is a programmed cell death triggered by recognition

of pathogen effectors or their markers.

We hypothesized that fungi recognize the invasion markers using the repeat domain of NLR proteins. The repeats are often highly conserved internally in each sequence, which allows for their fast rearrangement through the unequal crossing-over, a process up to 100,000 times quicker than the standard mutation.

In each family of NLR repeats (Ankyrin, TPR and WD40), we identified several positions which are highly variable despite overall high conservation of repeats. These positions, often found to be under positive selection, are expected to form the recognition paratopes quickly adapting to fast-evolving pathogens.

Repeat regions in NLR were decomposed to sequences of aminoacids at single highly variable sites. We simulated evolution of amino-acid sequences at each site using our stochastic string rewriting system with constraints, and compared results to real data consisting of 550 sequences.

The model explained the even-odd periodicity observed in the repeat number distribution of the TPR family of receptors. Moreover, in comparison to the simulated data, the amino-acid composition of real sequences revealed preferences consistent with the putative role of interacting paratope (bias towards polar residues, tyrosine and often tryptophan)

The approach also allows exploring solution space in order to find discrepancies between real and simulated data. In a preliminary study, we found a significantly overrepresented pattern at one position in the TPR family:

R-[SYQFW](1,3)-R.

References: Paoletti & Saupe (2009). Bioessays 31:1201. Chevanne et al. (2010) BMC Evolutionary Biology 10:134. Dyrka et al. (2014), Genome Biology and Evolution 6:3137.

Overview

The NLR family of receptors plays a key role in the innate immune

system of animals, plants and fungi. In the latter two phyla NLRs adapt

quickly to ever-changing pathogen-specific invasion markers thanks to

their repeat-based architecture, which can produce diversity of

recognition epitopes through unequal crossing-over and mutation.

Characterizing computationally the language of these pathogen

recognition receptors can provide insight into the molecular

mechanisms of immune response and describe the limits of the

pathogen

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pathogen targets that can be recognized. In this work, we model

generation and selection of the recognition paratopes as a stochastic

string rewriting system with constraints, tuned by analysis of observed

evolutionary processes, and validated with regard to a large dataset of

fungal NLRs. The methodology developed in this work is general and

therefore can be applied to any class of amino acid repeats generated

by unequal crossing-over for which an equivalent high quality dataset is

available.